Search Results

a2z-Unified-Triage is a radiological computer-aided triage and notification software indicated for use in the analysis of abdominal/pelvic CT images in adults aged 22 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of the 7 specified abdominopelvic findings: Acute Cholecystitis, Acute Pancreatitis, Unruptured Abdominal Aortic Aneurysm, Acute Diverticulitis, Free Air, Hydronephrosis, and Small Bowel Obstruction. These findings are intended to be used together as one device. The device supports both cloud-based and on-premises deployment, with integration either directly with healthcare facility systems or through third-party healthcare technology platforms.

a2z-Unified-Triage uses an artificial intelligence algorithm to analyze images and flag cases with detected findings in parallel to the ongoing standard of care image interpretation. The device provides analysis results that enable client systems to generate notifications for cases with suspected findings. These results can include DICOM instance UIDs for key images, which are meant for informational purposes only and not intended for primary diagnosis beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The results of a2z-Unified-Triage are intended to be used in conjunction with other patient information and based on clinicians' professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

a2z-Unified-Triage is a radiological computer-assisted triage and notification software device. The software consists of an algorithmic component that supports both cloud-based and on-premises deployment on standard server hardware. The device processes abdomen/pelvis CT images from clinical imaging systems, analyzing them using artificial intelligence algorithms to detect suspected cases of 7 abdominopelvic conditions: Acute Cholecystitis, Acute Pancreatitis, Unruptured Abdominal Aortic Aneurysm, Acute Diverticulitis, Free Air, Hydronephrosis, and Small Bowel Obstruction.

Following the AI processing, the analysis results are returned to the client system for worklist prioritization. When a suspected case is detected, the software provides analysis results that enable the client system to generate appropriate notifications. These results can include DICOM instance UIDs for key images, which are for informational purposes only, do not contain any marking of the findings, and are not intended for primary diagnosis beyond notification.

Integration with clinical imaging systems facilitates efficient triage by enabling prioritization of suspect cases for review of the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.

Here's a detailed summary of the acceptance criteria and the study proving the device meets them, based on the provided FDA clearance letter:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

a2z-Unified-Triage differentiates between two types of findings for regulatory purposes: QAS (Qualitative, Automated, and Subjective) and QFM (Quantitative, Functional, and Measurable).

Turnaround Time Acceptance Criteria and Performance:

2. Sample size used for the test set and the data provenance

• Test Set Sample Size: 675 cases from 643 unique patients (after excluding 3 cases due to quality control failures from an initial 678 cases).
• Data Provenance: The data was sourced from multiple clinical sites within the United States. Specific states mentioned are New York (45.2%), Kansas (21.2%), Missouri (18.4%), Texas (15.0%), and Nebraska (0.3%). The study evaluated against clinical standards consistent with U.S. practice patterns. The data appears to be retrospective, as it was used for development and testing after collection.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: A minimum of two U.S. board-certified radiologists, with a third U.S. board-certified expert adjudicator for discordant cases.
• Qualifications: All experts were U.S. board-certified radiologists. The third adjudicator was specifically fellowship-trained in body imaging.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: 2+1 methodology. Each case was independently reviewed by two U.S. board-certified radiologists. If the two initial readers disagreed, a third U.S. board-certified expert adjudicator (fellowship-trained in body imaging) provided the tie-breaking determination.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• The provided document does not indicate that an MRMC comparative effectiveness study was performed or submitted for this clearance. The study described is a standalone performance assessment of the algorithm itself against ground truth.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Yes, a standalone performance assessment was done. The document explicitly states: "A standalone performance assessment was performed for a2z-Unified-Triage to validate the accuracy of detecting the 7 findings against a reference standard established by U.S. board-certified radiologists."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: Expert consensus, specifically a 2+1 consensus of U.S. board-certified radiologists, with the third adjudicator being fellowship-trained in body imaging.

8. The sample size for the training set

• The document states, "The algorithms were developed on an extensive dataset of abdomen/pelvis CT studies from multiple clinical sites." However, a specific numerical sample size for the training set is not provided. It only mentions that strict protocols ensured complete independence between development and testing datasets (mutually exclusive patients).

9. How the ground truth for the training set was established

• The document does not explicitly detail how the ground truth for the training set was established. It only describes the ground truth establishment for the test set (2+1 radiologist consensus). It states that the algorithms were developed on an "extensive dataset" and implies internal processes for data collection and annotation during development.

Ask a specific question about this device